Hi,

I got this code from My trainner.He asked me to debug.I tried my level best to debug. but still it shows the K meanspoints class needed error. This alone i could not solve. I also tried with vector class, but it shows error.
please help me.

/*
  Implements the k-means algorithm
 */

import java.io.*;
import java.util.*;
 
 
 
/**
  Implements the k-means algorithm
  */ 
public class kMeans {


	/** Number of clusters */
	private int k;
	

	/** Array of clusters */
	private cluster[] clusters;
	
	
	/** Number of iterations */
	private int nIterations;
	
	
	/** Vector of data points */
	private Vector kMeansPoints;
	
	
	/** Name of the input file */
	private String inputFileName;
	
	
	/**
	 * Returns a new instance of kMeans algorithm
	 *
	 * @param	k		number of clusters
	 * @param	inputFileName	name of the file containing input data
	 */
         public kMeans(int k, String inputFileName) {
	
		this.k = k;
		this.inputFileName = inputFileName;
		this.clusters = new cluster[this.k];
		this.nIterations = 0;
		this.kMeansPoints = new Vector();
	
	} // end of kMeans()


	/**
	 * Returns a new instance of kMeans algorithm
	 *
	 * @param	k		number of clusters
	 * @param	kMeansPoints	List containing objects of type kMeansPoint
	 */
         public kMeans(int k, List kMeansPoints) {
	
		this.k = k;
		this.inputFileName = inputFileName;
		this.clusters = new cluster[this.k];
		this.nIterations = 0;
		this.kMeansPoints=new Vector(kMeansPoints);
	
	} // end of kMeans()
	
	
	/**
	 * Reads the input data from the file and stores the data points in the vector
	 */
	public void readData() throws IOException{
	
		BufferedReader in = new BufferedReader(new FileReader(this.inputFileName));
		String line = "";
		while ((line = in.readLine()) != null ){
                        
			StringTokenizer st = new StringTokenizer(line, " \t\n\r\f,");
				if (st.countTokens() == 2) {
					
					kMeansPoint dp = new kMeansPoint(Integer.parseInt(st.nextToken()), Integer.parseInt(st.nextToken()));
					dp.assignToCluster(0);
					this.kMeansPoints.add(dp);
                        
                        }
                        
		}
		
		in.close();
	
	} // end of readData()
	
	
	/**
	 * Runs the k-means algorithm over the data set
	 */
	public void runKMeans() {
	
		// Select k points as initial means
		for (int i=0; i < k; i++){
		
			this.clusters[i] = new cluster(i);
			this.clusters[i].setMean((kMeansPoint)(this.kMeansPoints.get((int)(Math.random() * this.kMeansPoints.size()))));
		
		}
		
		
		do {
			// Form k clusters
			Iterator i = this.kMeansPoints.iterator();
			while (i.hasNext())
				this.assignToCluster((kMeansPoint)(i.next()));
				
			this.nIterations++;
		
		}
		// Repeat while centroids do not change
		while (this.updateMeans());
	
	} // end of runKMeans()
	
	
	/**
	 * Assigns a data point to one of the k clusters based on its distance from the means of the clusters
	 *
	 * @param	dp	data point to be assigned
	 */
	private void assignToCluster(kMeansPoint dp) {
	
		int currentCluster = dp.getClusterNumber();
		double minDistance = kMeansPoint.distance(dp, this.clusters[currentCluster].getMean());;
		
		for (int i=0; i <this.k; i++)
			if (kMeansPoint.distance(dp, this.clusters[i].getMean()) < minDistance) {
		
				minDistance = kMeansPoint.distance(dp, this.clusters[i].getMean());
				currentCluster = i;
				
			}
		
		dp.assignToCluster(currentCluster);	
	
	} // end of assignToCluster
	
	
	/**
	 * Updates the means of all k clusters, and returns if they have changed or not
	 *
	 * @return	have the updated means of the clusters changed or not
	 */
	private boolean updateMeans() {
	
		boolean reply = false;
		
		int[] x = new int[this.k];
		int[] y = new int[this.k];
		int[] size = new int[this.k];
		kMeansPoint[] pastMeans = new kMeansPoint[this.k];
		
		for (int i=0; i<this.k; i++) {
		
			x[i] = 0;
			y[i] = 0;
			size[i] = 0;
			pastMeans[i] = this.clusters[i].getMean();
		
		}
		
		Iterator i = this.kMeansPoints.iterator();
		while (i.hasNext()) {
		
		
			kMeansPoint dp = (kMeansPoint)(i.next());
			int currentCluster = dp.getClusterNumber();
			
			x[currentCluster] += dp.getX();
			y[currentCluster] += dp.getY();
			size[currentCluster]++;
		
		}
		
		for (int j=0; j < this.k; j++ ) 
			if(size[j] != 0) {
			
				x[j] /= size[j];
				y[j] /= size[j];
				kMeansPoint temp = new kMeansPoint(x[j], y[j]);
				temp.assignToCluster(j);
				this.clusters[j].setMean(temp);
				if (kMeansPoint.distance(pastMeans[j], this.clusters[j].getMean()) !=0 )
					reply = true;
					
			}
		
		return reply;
		
	} // end of updateMeans()


	/**
	 * Returns the value of k
	 *
	 * @return	the value of k
	 */
	public int getK() {

		return this.k;

	} // end of getK()
	
	
	/**
	 * Returns the specified cluster by index
	 *
	 * @param	index	index of the cluster to be returned
	 * @return	return the specified cluster by index
	 */
	public cluster getCluster(int index) {
	
		return this.clusters[index];
	
	} // end of getCluster()
        
        
	/**
	 * Returns the string output of the data points
	 *
	 * @return  the string output of the data points
	 */
	public String toString(){
            
		return this.kMeansPoints.toString();
            
	} // end of toString()
        
        
	/**
	 * Returns the data points
	 *
	 * @return  the data points
	 */
	public Vector getDataPoints() {
            
		return this.kMeansPoints ;
            
	} // end of getDataPoints()
        
        
	/**
	 * Main method -- to test the kMeans class
	 *
	 * @param   args    command line arguments
	 */
	public static void main(String[] args) {
            
		kMeans km = new kMeans(2, "input1");
		
		try {
			km.readData();
		} catch (Exception e) {
			System.err.println(e);
			System.exit(-1);
		}
            
		km.runKMeans();
		System.out.println(km);
                    
        } // end of main()

} // end of class


/*
 * Represents an abstraction for a cluster of data points in two dimensional space
 */
 

 
/**
 * Represents an abstraction for a cluster of data points in two dimensional space
  */
 class cluster {


	/** Cluster Number */
	private int clusterNumber;
	
	
	/** Mean data point of this cluster */
	private kMeansPoint mean;
	
	
	/**
	 * Returns a new instance of cluster
	 *
	 * @param	_clusterNumber	the cluster number of this cluster
	 */
	public cluster(int _clusterNumber) {
	
		this.clusterNumber = _clusterNumber;
		
	} // end of cluster()
	
	
	/**
	 * Sets the mean data point of this cluster
	 *
	 * @param	meanDataPoint	the new mean data point for this cluster
	 */
	public void setMean(kMeansPoint meanDataPoint) {
	
		this.mean = meanDataPoint;
	
	} // end of setMean()
	
	
	/**
	 * Returns the mean data point of this cluster
	 *
	 * @return	the mean data point of this cluster
	 */
	public kMeansPoint getMean() {
	
		return this.mean;
	
	} // end of getMean()
	
	
	/**
	 * Returns the cluster number of this cluster
	 *
	 * @return	the cluster number of this cluster
	 */
	public int getClusterNumber() {
	
		return this.clusterNumber;
	
	} // end of getClusterNumber()
	

	/**
	 * Main method -- to test the cluster class
	 *
	 * @param	args	command line arguments
	 */
	public static void main(String[] args) {
	
		cluster c1 = new cluster(1);
		c1.setMean(new kMeansPoint(3,4));
		System.out.println(c1.getMean());

	
	} // end of main()

} // end of class

plz help me it is urgent

Why do you think your trainer gave this to you to be debugged, only for you to pass it onto somebody else, naah, he wants YOU to do that, so probably there's something in it that he wants you to learn. Step through the program, see whats it expected to do, observe what is actually does, do some figuring out using dry running etc to find out the reason for the difference. I think this help should be enough for you to start working on it.

Well, where the heck is kMeansPoint defined? Not in that code.

Also, if you are going to ask on help with an error, post the entire error and give at least some indication of where in the code that error takes place.

@masijade:

but still it shows the K meanspoints class needed error

Thats the error he is looking to sort out. And look at the solution he is trying,

I also tried with vector class, but it shows error.

Thats a simple error he has been asked to chuck off by his tutor. That explains my previous post.

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